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  <div class="headertitle"><div class="title">LBFGS.h</div></div>
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<a href="_l_b_f_g_s_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       LBFGS</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * The Limited-Memory Broyden, Fletcher, Goldfarb, Shannon (BFGS) algorithm</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * is a quasi-Newton method for unconstrained real-valued optimization.</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * See: http://en.wikipedia.org/wiki/LBFGS for details.</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * </span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \author      S. Dahlgaard, O.Krause</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \date        2013</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> *</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> *</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * </span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> * </span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="comment"> *</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="comment"> */</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#ifndef SHARK_ML_OPTIMIZER_LBFGS_H</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#define SHARK_ML_OPTIMIZER_LBFGS_H</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_line_search_optimizer_8h.html" title="Base class for Line Search Optimizer.">shark/Algorithms/GradientDescent/AbstractLineSearchOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;deque&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"></span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \brief Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// BFGS is one of the best performing quasi-newton methods. However for large scale</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// optimization, storing the full hessian approximation is infeasible due to O(n^2) memory requirement.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// The L-BFGS algorithm does not store the full hessian approximation but only stores the</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// data used for updating in the last steps. The matrix itself is then regenerated on-the-fly in</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// an implicit matrix scheme. This brings runtime and memory rquirements</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// of a single step down to O(n*hist_size).</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// The number of steps stored can be set with setHistCount. This is 100 as default.</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// The algorithm is implemented for unconstrained and constrained optimization</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// in the case of box constraints. When box constraints are present and the algorithm</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// encounters a constraint, a dog-leg style algorithm is used:</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// first, all variables with active constraints (e.g. x_i = l_i and g_i &gt; 0)</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// are fixed, i.e. p_i = 0. For the remaining variables, the unconstrained optimization</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// problem is solved. If the solution does not statisfy the box constraints, in the next step</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">/// the cauchy point is computed. If the cauchy point is feasible, we search the point</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// along the line between unconstrained optimum and cauchy point that lies exactly on the constraint.</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// This is the point with smallest value along the path.</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// This does not find the true optimal step in the unconstrained problem, however a cheap and reasonably good optimum</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">/// which often improves over naive coordinate descent.</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">/// \ingroup gradientopt</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> SearchPo<span class="keywordtype">int</span>Type = RealVector&gt;</div>
<div class="foldopen" id="foldopen00073" data-start="{" data-end="};">
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html">   73</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_l_b_f_g_s.html" title="Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm.">LBFGS</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_line_search_optimizer.html" title="Basis class for line search methods.">AbstractLineSearchOptimizer</a>&lt;SearchPointType&gt;{</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#a547abec1b43a6f6ad1a06749dc3c217f">   75</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_line_search_optimizer.html#a444f639715ef1d69077760f88ff724a2">AbstractLineSearchOptimizer&lt;SearchPointType&gt;::ObjectiveFunctionType</a> <a class="code hl_typedef" href="classshark_1_1_l_b_f_g_s.html#a547abec1b43a6f6ad1a06749dc3c217f">ObjectiveFunctionType</a>;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span> </div>
<div class="foldopen" id="foldopen00077" data-start="{" data-end="}">
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#af1a1c3e07e77e4183383cd506ce121da">   77</a></span>    <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#af1a1c3e07e77e4183383cd506ce121da">LBFGS</a>() :m_numHist(100){</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_optimizer.html#a72daf583d406e144b90869f311baa594">m_features</a> |= this-&gt;<a class="code hl_enumvalue" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0ab95c65f700f2158f39039d8f580d350f">CAN_SOLVE_CONSTRAINED</a>;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    }</div>
</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment"></span> </div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00082" data-start="{" data-end="}">
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#a9166b5a6d3a92af54895ab7308223de2">   82</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#a9166b5a6d3a92af54895ab7308223de2" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;LBFGS&quot;</span>; }</div>
</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="comment"></span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    ///  \brief Specify the amount of steps to be memorized and used to find the L-BFGS direction.</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    ///\param numhist The amount of steps to use.</span></div>
<div class="foldopen" id="foldopen00088" data-start="{" data-end="}">
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#a57f205e2184f5a1c4d57a6ac99ec94fa">   88</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#a57f205e2184f5a1c4d57a6ac99ec94fa" title="Specify the amount of steps to be memorized and used to find the L-BFGS direction.">setHistCount</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numhist) {</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(numhist &gt; 0, <span class="stringliteral">&quot;An empty history is not allowed&quot;</span>);</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        m_numHist = numhist;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    }</div>
</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span> </div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="comment">//from ISerializable</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#adbf39210c8d5255385700e2f5fb6e019">   94</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#adbf39210c8d5255385700e2f5fb6e019" title="Read the component from the supplied archive.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp;archive);</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#ab5341c7990a3411e81bf04dd00b0983d">   95</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#ab5341c7990a3411e81bf04dd00b0983d" title="Write the component to the supplied archive.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp;archive) <span class="keyword">const</span>;</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="keyword">protected</span>: <span class="comment">// Methods inherited from AbstractLineSearchOptimizer</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#aeee8fae6dba7fd99c3e4099fe6427797">   97</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#aeee8fae6dba7fd99c3e4099fe6427797" title="Initializes the internal model.">initModel</a>();</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_l_b_f_g_s.html#a2cf819c6eb6e886395ae71df403edc6e">   98</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_l_b_f_g_s.html#a2cf819c6eb6e886395ae71df403edc6e" title="Updates the Model and computes the next search direction.">computeSearchDirection</a>(<a class="code hl_typedef" href="classshark_1_1_l_b_f_g_s.html#a547abec1b43a6f6ad1a06749dc3c217f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp;);</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="keyword">private</span>:<span class="comment"></span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment">    ///\brief Stores another step and searchDirection, discarding the oldest on if necessary.</span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// \param step Last performed step</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">    /// \param y difference in gradients</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment"></span>    <span class="keywordtype">void</span> updateHist(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a>&amp; y, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> &amp;<a class="code hl_function" href="classshark_1_1_abstract_line_search_optimizer.html#ae6689563bafd7dbbb02299e161238b26">step</a>);<span class="comment"></span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// \brief Compute B^{-1}x</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// The history is used to define B which is easy to invert</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment"></span>    <span class="keywordtype">void</span> multBInv(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a>&amp; searchDirection)<span class="keyword">const</span>;</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment"></span> </div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    /// \brief Compute Bx</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment"></span>    <span class="keywordtype">void</span> multB(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a>&amp; searchDirection)<span class="keyword">const</span>;</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment"></span> </div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment">    /// \brief Get the box-constrained LBFGS direction. </span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment">    /// Approximately solves the constrained optimization problem</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment">    /// min_p 1/2 p^TBp +g^Tp</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment">    /// s.t. l_i &lt;= x_i + p_i &lt;= u_i</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// This is done using a constrained dogleg approach.</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    /// first, all variables with active constraints (e.g. x_i = l_i and g_i &gt; 0)</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    /// are fixed, i.e. p_i = 0. For the remaining variables, the unconstrained optimization</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    /// problem is solved. If the solution does not statisfy the box constraints, in the next step</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    /// the cauchy point is computed. If the cauchy point is feasible, we search the point</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// along the line between unconstrained optimum and cauchy point that lies exactly on the constraint.</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    /// This is the point with smallest value along the path.</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment"></span>    <span class="keywordtype">void</span> getBoxConstrainedDirection(</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a>&amp; searchDirection,</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; lower,</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; upper</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    )<span class="keyword">const</span>;</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span> </div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    <span class="keywordtype">double</span> m_updThres;<span class="comment">///&lt;Threshold for when to update history.</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_numHist; <span class="comment">///&lt; Number of steps to use for LBFGS.</span></div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    <span class="comment">// Initial Hessian approximation. We use a diagonal matrix, where each element is</span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    <span class="comment">// the same, so we only need to store one double.</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>    <span class="keywordtype">double</span>          m_bdiag;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span> </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    <span class="comment">// Saved steps for creating the approximation.</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    <span class="comment">// Use deque as it gives fast pop.front, push.back and access. Supposedly.</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    <span class="comment">// steps holds the values x_(k+1) - x_k</span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    <span class="comment">// gradientDifferences holds the values g_(k+1) - g_k</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>    std::deque&lt;SearchPointType&gt; m_steps;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    std::deque&lt;SearchPointType&gt; m_gradientDifferences;  </div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>};</div>
</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">//implementation is included in the library</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_l_b_f_g_s.html" title="Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm.">LBFGS&lt;RealVector&gt;</a>;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="keyword">extern</span> <span class="keyword">template</span> <span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_l_b_f_g_s.html" title="Limited-Memory Broyden, Fletcher, Goldfarb, Shannon algorithm.">LBFGS&lt;FloatVector&gt;</a>;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span> </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>}</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="preprocessor">#endif</span></div>
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